Sample Submission GuidelinesWhat Is RAD-seq
RAD-seq, short for Restriction-site associated DNA sequencing, represents an inaugural iteration of next-generation sequencing methodologies devised to streamline genome sequencing by concentrating on precise genomic enzyme cleavage sites. This sophisticated approach harnesses restriction endonucleases for the targeted cleavage of the genome, yielding fragments of predetermined lengths. Following this genomic fragmentation, meticulously crafted sequencing libraries are prepared, and the resultant RAD tags are subjected to high-throughput sequencing procedures.
RAD-seq, an illustrious exemplar of streamlined genomics methodologies, had its inception in 2008 when Baird and collaborators from Cornell University pioneered its development. This groundbreaking technique harnessed the SbfI (CCTGCA^GG) enzyme to precisely cleave genomic sequences, yielding approximately 50 base pairs of sequence on either side of the enzyme cleavage sites, subsequently enabling marker genotyping.
RAD-Seq stands out as an optimal choice for comprehensive large-scale investigations, establishing a robust platform for profound information extraction via whole-genome resequencing protocols. In contrast to conventional chip-based genotyping methodologies, RAD-Seq excels in its capacity to uncover novel single-nucleotide polymorphisms (SNPs) within hitherto unexplored genomic variations, encompassing rare and emerging variants. The versatility of RAD-Seq technology extends to diverse applications, spanning variant discovery, genetic map construction, functional gene exploration, population evolutionary analyses, and beyond. This technology not only provides a theoretical framework for subsequent explorations into evolutionary relationships and functional gene identification but also holds substantial significance in the realms of both research and industry.
Workflow of RAD-seq
RAD-seq entails a structured series of stages, including:
Genomic DNA fragmentation employing restriction endonucleases.
Adjoining P1 adapters to the fragmented DNA.
Aggregating and randomly fragmenting enzyme-cleaved segments from all samples.
Culling fragments of appropriate dimensions (typically in the range of 300 to 700 base pairs).
Ligating P2 adapters to the selected fragments.
Implementing PCR amplification for enrichment.
Technical Parameters
| Product Name | Sequencing Platform | Parameter Specifications |
| RAD-Seq-Variation Detection | HiSeq PE150 | ≥1X per individual (≥5X for assembly samples) |
| RAD-Seq-Genetic Mapping | HiSeq PE150 | Parent: 2-5X, Offspring: 0.8X per individual (F1, F2); 0.6X per individual (RIL, DH) |
| RAD-Seq-Population Evolution | HiSeq PE150 | ≥1X per individual (5X for assembly samples) |
Other related services
Technical Advantages
Extensive experience in analyzing large animal and plant genomes.
Integration of comprehensive genomics analysis, offering various omics technology services, including genomics, transcriptomics, and epigenetics, as well as advanced and customized genomics analysis solutions.
Streamlined processes with shorter project cycles.
Cost-saving through joint analysis of sample information with public databases.
Sample Requirements
1. Variant Detection:
DNA sample quantity: ≥ 3 µg.
Digestion with EcoRI (GAATTC).
Recommended sequencing depth: ≥ 1X per sample (≥ 5X for assembly samples).
2. Genetic Mapping:
DNA sample quantity: ≥ 3 µg.
Sequencing depth: Parents 2-5X, Offspring 0.8X per individual (F1, F2, etc., temporary populations), 0.6X per individual (RIL, DH, etc., permanent populations).
Applicable to: Haploid or diploid species, all mapping populations (F1, F2; RIL, DH, etc.), with populations of 100 individuals or more.
3. Population Evolution:
DNA sample quantity: ≥ 3 µg.
Sequencing depth: ≥ 1X per individual (≥ 5X for assembly samples).
Applicable to: Different subpopulations within haploid or diploid species, distinct subpopulation divisions, representative individuals within the same subpopulation. Approximately 10 samples per subpopulation (for animals ≥ 10, for plants ≥ 15), with a total of at least 30 samples.
4. Sample Details:
DNA samples: Please provide DNA with a concentration > 100 ng/μl and a total amount > 3 μg, OD 260/280 ratio between 1.8 and 2.0.
Ensure that the DNA is not degraded, shows no significant RNA bands upon electrophoresis, and has clear, intact genomic bands, with the main band above 100 kb. Contamination by polysaccharides or glycoproteins can pose significant challenges to DNA fragmentation, making removal difficult. Therefore, it is essential that provided samples are free from polysaccharide or glycoprotein contamination.
Plant samples: Select young plant tissues, with each sample weighing > 500 mg. Store and ship the samples on dry ice or in liquid nitrogen.
Animal samples: Fresh animal tissues are required, avoiding fatty tissues, with each sample weighing > 50 mg.
For common species, select tissues such as liver, kidney, and blood. For rare species, provide ear samples or hair samples (with hair roots) with lower fat content. To minimize the impact of individual variation on subsequent assembly, it is preferable to sample from the same individual. If the species are small in size and the DNA extraction quantity from one individual is insufficient for sequencing, try to reduce the number of sampled individuals while maintaining the required amount. Provide tissue samples of > 50 mg; provide a sufficient quantity, considering different species may yield varying amounts of DNA upon extraction.
Q: What considerations are paramount in the development of high-density SNP markers?
A: The essential prerequisites and guiding principles for the development of high-density SNP markers revolve around the imperative of achieving a balanced distribution of fragments throughout the genome. By identifying sequences that faithfully represent comprehensive genomic information, an array of thousands of SNP markers can be systematically generated. In the initial stages, bioinformatics techniques are deployed to methodically assess the reference genome of the target species (or known BAC sequences). Guided by factors such as genome GC content, repetitive sequence characteristics, and gene attributes, discerning choices of appropriate restriction enzymes and sequencing library types are made, with the primary objective of ensuring that the density, consistency, and overall efficacy of molecular markers align with the stringent requisites of genetic analysis and molecular breeding.
Q: What are the application domains of RAD-seq?
A: RAD-seq finds versatile utility in diverse research areas encompassing the establishment of genetic maps, phylogeographic analysis of polymorphisms, association studies, and QTL mapping, irrespective of the presence or absence of reference genome data for a given species. The broad reach of RAD-Seq technology extends to investigations involving variation discovery, the construction of genetic maps, the exploration of functional genes, and the study of population evolution.
Q: What are the merits of employing RAD-seq in the context of population evolution studies?
A: Population evolution studies typically encompass a considerable number of specimens. RAD-seq presents a set of distinct advantages in this regard. It effectively diminishes genome complexity, resulting in reduced expenditures on library preparation and sequencing. Its straightforward workflow streamlines the process. Furthermore, its independence from reference genomes renders it applicable to a broader spectrum of species. Notably, RAD-Seq excels in investigations involving extensive sample sizes, thereby furnishing a robust framework for comprehensive data extraction through whole-genome resequencing technologies.
Q: What distinguishes the utilization of a reference genome from its absence?
A: When it comes to the identification of mutational information, the incorporation of a reference genome for sequence alignment and variation detection typically yields heightened levels of both efficiency and precision. Additionally, it facilitates the precise localization of specific genes and enables investigations into population linkage disequilibrium.
Community Structure
SNP Mutation Spectrum
SNP Mutation Spectrum
Genetic Map Linkage Groups
Trait Association Analysis
